The field of medical imaging has seen significant advances since the time X-ray images were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as medical resonance imaging (MRI) scanners, computed axial tomography (CAT) scanners, etc. Due to the large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Recognition and processing of specific meaningful structures within a medical image may be referred to as automated advanced post-processing, which is a type of post-processing for medical imaging applications. One particularly useful image processing technique involves the imaging of the spinal column. A precise vertebra segmentation and identification method is in high demand due to its importance to, and impact on, many orthopedic, neurological, and ontological applications. For example, during the interpretation of spinal images, the radiologist often faces the tedious task of having to determine the level of spine and report the location of findings in terms of the cervical, thoracic and lumbar vertebrae or disk. In order to determine which vertebra is affected, the radiologist typically has to scroll and switch between sagittal and axial images many times. Without the aid of automated systems, such process is often very time consuming and error-prone.
Unfortunately, the task of segmenting and labeling vertebrae, even using automated post-processing techniques that are well-developed for other anatomical structures, often proves to be inaccurate and therefore inadequate. The difficulty lies in the inherent complexity of vertebrae. The variation within the same class of vertebra as well as the variation in neighboring structures makes vertebral modeling and imaging extremely difficult. Labeling becomes even more complicated in atypical cases where the vertebrae (or other spinal structures) have unusual characteristics (e.g., number, width, shape, size, etc.). In addition, imperfect image acquisition processes may result in noisy or incomplete scans that compound the difficulties of ascertaining the total number and positions of the vertebrae. Therefore, vertebrae often have to be manually labeled and corrected by the radiologist to ensure accuracy. This verification process, however, is also extremely time-consuming and error-prone, typically involving repeated scrolling of multiple images to check rib connections (e.g., lumbarization, sacralization, 11 or 13 T-spine, etc.) and labels of the vertebrae.
Accordingly, there is a need for improved systems and methods to facilitate efficient evaluation, labeling, and analysis of the spinal column and other anatomical structures.